The setting of production targets for various types of production facilities has been effectuated with the use of real time optimization programs that utilize an optimization technology. Such programs or programming is used as a planning aid to determine the production output from the facilities that will optimize production on the basis of the cost of production and the selling price of the products to be produced.
For example, in U.S. Pat. No. 7,092,893, a method of controlling liquid and gaseous production within a number of air separation plants is provided in which gaseous and liquid products are produced and distributed to customers. In an air separation plant, air is separated by compressing the air, cooling the compressed air to a level at or near its dew point and then distilling the air in distillation columns to produce such gaseous products as oxygen and nitrogen and liquid products such as liquid oxygen. Hence, a central cost of production in air separation plants is the electrical power costs that are consumed in compressing the air in producing liquid products. The electrical power costs will vary from plant to plant and will in any case vary based upon the time of day that the electrical power is consumed. Additionally, road shipping costs present another variable that depends upon the location of the production facility and the customer. In order to maximize the profits and to meet customer demands, the particular producing plants are selected to minimize electrical power costs and road shipping costs.
A particularly complex optimization problem concerns the operation of facilities that employ chemical processes, for example, steam methane reforming to produce hydrogen containing products such as a synthesis gas or a further refined hydrogen product as well as export steam that can also be sold as a product. The raw materials that are consumed are natural gas, makeup water to generate the steam and depending upon the plant, electrical power.
The complexity of the optimization results from the number of unit operations that are being conducted in such a plant. For example, part of the natural gas and part of the steam is fed to a steam methane reformer that consumes the natural gas and steam in endothermic steam methane reforming reactions. A remaining part of the natural gas and combustion air are consumed within a radiant heat exchange zone of the steam methane reformer to generate the heat necessary to support the endothermic heating requirements of the steam methane reforming reactions. There is further a complicated network of heat exchangers that are used to generate steam from the makeup water. The resultant synthesis gas can then be sent to a water gas shift reactor to increase the hydrogen content of the synthesis gas and then to a pressure swing adsorption unit to separate the hydrogen into the hydrogen product. A hydrogen containing stream produced from the cryogenic rectification process can be combined with that produced from the water gas shift reactor for separation within the pressure swing adsorption system and the production of the hydrogen product.
Such hydrogen producing plants are directly controlled by setting targets for a control system that in turn controls the process parameters that are relevant to the particular unit operation involved. For example, changing the steam to carbon ratio of the reactants fed to the steam methane reforming process will change the hydrogen content in the synthesis gas. Also, the amount of the synthesis gas sent to the water gas shift reactor will also effect the amount of hydrogen and carbon monoxide to be recovered as products. Typically, the controller can utilize model predictive control techniques to set the control targets to obtain a particular desired plant performance, for example, to produce a specific amount of hydrogen.
In any chemical plant or other production facility, it is desirable to set production targets to obtain the maximum profitability or margin based upon the selling price of the products and the costs of production, for example, electrical power, natural gas and water costs in case of a hydrogen plant. Complicating any calculation of the production products of the products being offered for sale is that the demand for such products will vary resulting in a variable profitability or margin. Contributing to such variability is that the costs of the raw materials can vary. Additionally, many of such production facilities can employ similar processes that produce the same intermediate products, for example, several steam methane reformers that are of different design or capability.
It would therefore appear that the setting of such production targets as inputs to the control system would be amenable to real time optimization techniques that can handle many variables and arrive at an optimization that individually considers movement of each variable. As stated previously, the use of such real time optimization techniques has found applicability as a planning tool. This is due to the fact that models used for the unit operations that calculate process outputs such as the products produced by chemical reactions of raw materials are themselves complex and take a sufficiently long time to converge. As a result the real time optimization cannot be practically utilized for purposes of setting targets for control of the plant. For example, a model of a steam methane reformer typically will model the heat transfer within the radiant section along the length of each of the reformer tubes and will calculate the chemical reaction along the length of the reformer tubes. Such a model can take an hour or more of computational time. A model predictive control system, however, updates targets every minute.
As will be discussed, the present invention provides a method of controlling the plant operations in which variable margin is optimized by real time optimization techniques that utilize semi-empirical process models in connection with such techniques that make practical the setting of target for control of a plant by real time optimization.